Optimal TDMA Frame Scheduling in Broadcasting Packet Radio Networks Using a Gradual Noisy Chaotic Neural Network

• Published in: Advances in Natural Computation pp. 1080 - 1089
• Main Authors: Shi, Haixiang, Wang, Lipo
• Format: Book Chapter Conference Proceeding
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2005; Springer
• Series: Lecture Notes in Computer Science
• Subjects: Applied sciences; Artificial intelligence; Channel Utilization; Computer science; control theory; systems; Exact sciences and technology; Frame Length; Time Division Multiple Access; Time Slot; Travel Salesman Problem; Chaos; Hopfield neural nets; Energy method; Packet switching; Energy function; Neural network; Time division multiple access; Genetic algorithm; Time average; Delay time; Radio networks; Collision avoidance; Hopfield model; Radio communication
• ISBN: 3540283234; 9783540283232
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/11539087_145

In this paper, we propose a novel approach called the gradual noisy chaotic neural network (G-NCNN) to find a collision-free time slot schedule in a time division multiple access (TDMA) frame in packet radio network (PRN). In order to find a minimal average time delay of the network, we aim to find an optimal schedule which has the minimum frame length and provides the maximum channel utilization. The proposed two-phase neural network approach uses two different energy functions, with which the G-NCNN finds the minimal TDMA frame length in the first phase and the NCNN maximizes the node transmissions in the second phase. Numerical examples and comparisons with the previous methods show that the proposed method finds better solutions than previous algorithms. Furthermore, in order to show the difference between the proposed method and the hybrid method of the Hopfield neural network and genetic algorithms, we perform a paired t-test between two of them and show that G-NCNN can make significantly improvements.